Patterns of epileptiform activity in kainate injected mice : : detection, dynamics, and anatomical correlates

Abstract: In epilepsy, patients suffer from hypersynchronized brain activity. Such epileptiform activity (EA) can present itself in diverse patterns. Research thus far focused on the two most extreme versions, epileptiform spikes and seizures, leaving the wide range of patterns between these poles ill-defined and underexplored. Our goal was to better describe and understand the wide range of EA patterns in the intrahippocampal kainate mouse model of mesial temporal lobe epilepsy. Specifically, we aimed (1) to comprehensively detect and classify EA patterns, (2) to identify relationships between EA patterns and anatomical pathology, as it manifests throughout epileptogenesis and hippocampal sclerosis, and (3) to explore the dynamic interplay of EA patterns.
We analyzed local field potentials recorded in vivo from the sclerotic hippocampus of chronically epileptic mice and developed a tool to automatically detect and classify EA patterns within a unified, data-based framework. The feature distributions of EA patterns suggested a continuum of EA in terms of spike load. We partitioned this continuum into high-load, medium-load, and low-load bursts. The rate of high-load bursts was anti-correlated to markers of anatomical pathology, suggesting that the strongest EA may be generated in less sclerotic parts. When investigating the dynamic interplay between EA categories, we identified three macro-patterns: high-load clusters, transition phases, and background phases. In these phases, low-load bursts played a dual role. On the one hand, their rate was increased in transition phases surrounding high-load clusters. On the other hand, high rates of low-load bursts in background phases indicated reduced susceptibility to high-load bursts. This suggests that low-load bursts could serve as a context-dependent marker for the susceptibility to high-load bursts.
Our approach resulted in a refined quantification and an improved understanding of EA and highlighted the fruitfulness of analyzing bursts patterns in epilepsy. Our results pointed towards complex causal relationships and yielded testable mechanistic hypotheses which might open new therapeutic avenues

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch
• Notes: Universität Freiburg, Dissertation, 2020

Classification and Topics

• Keyword: Mice ; Dynamics ; Epilepsie ; Datenanalyse ; Explorative Datenanalyse ; Klassifikation ; Maus

Contributors, Places and Time

• Event: Veröffentlichung
• (where): Freiburg
• (who): Universität
• (when): 2020
• Creator: Heining, Katharina
• Contributor: Egert, Ulrich ; Albert-Ludwigs-Universität Freiburg. Fakultät für Biologie

Further information

• DOI: 10.6094/UNIFR/170093
• URN: urn:nbn:de:bsz:25-freidok-1700930
• Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 25.03.2025, 1:48 PM CET